import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
from plotly.subplots import make_subplots
#bring in data
data_dir = "../data/"
#bring in temporal plant hieght data (weilbull fit) from Variance Components r script
temp_ht = pd.read_csv(data_dir+"Tpht_subset.csv", index_col=[0])
#bring in gdds
gdd = pd.read_csv("../data/GDDs_DAPs.csv", index_col=[0], parse_dates=[0])
#add in GDDS
temp_ht = temp_ht.merge(gdd[["DAP","Cum_GDD [C]"]], on="DAP")
#flowering windows
flower_DAP = (51.19470817271569, 57.2200534210139)
flower_GDD = (750.1523934725153, 838.2223942479286)
man_VCs = pd.read_csv("../data/Var_comps_manual_phenos_Tpht.csv", index_col=[0])
#fix naming
man_VCs["grp"] = man_VCs["grp"].str.replace("Pedigree","Hybrid")
man_VCs.rename(columns={"grp":"Variance Components", "Percent":"Explaned Percent Variation (%)",
"Heritability":"Repetability"}, inplace=True)
man_calc_BLUPS = pd.read_csv("../data/blups_manual_phenos.csv", index_col=[0])
#plot heights by date and value
def plot_heights(df_stacked, flower_DAP=flower_DAP, x_col="DAP", value_col="Height", color="Tester",
testers_to_keep=['PHK76','PHP02','PHZ51'], xaxis_title = "Days After Planting (DAP)",
save_html_path="", save_svg_path="", fig_width=2000, fig_height=800):
#filter testers
for_fig = df_stacked[df_stacked["Tester"].isin(testers_to_keep)].copy()
print("Number of Hybrids: ", len(df_stacked["Pedigree"].unique()), len(for_fig["Pedigree"].unique()))
fig = px.box(for_fig, x = x_col, y=value_col, color=color)
fig.update_layout(margin=dict(l=0, r=1, t=30, b=1))
fig.update_layout(xaxis_title=xaxis_title)
fig.update_layout(yaxis_title="Plant Height")
#add flowering time box
min_range = -0.4
max_range= 2.6
fig.update_yaxes(range=[min_range, max_range])
fig.add_shape(type="rect", x0=flower_DAP[0], y0=min_range, x1=flower_DAP[1], y1=max_range,
line=dict(
color="black",
width=3,
), fillcolor="black", opacity=0.25)
fig.update_layout(legend=dict(yanchor="bottom",
y=0.05,
xanchor="right",
x=0.99))
fig.show()
fig.update_layout(autosize=False, width=fig_width, height=fig_height)
if save_html_path != "":
fig.write_html(save_html_path)
if save_svg_path != "":
fig.write_image(save_svg_path)#, scale=scale)
#return df_stacked
#FIGURE 3A
plot_heights(temp_ht, x_col="DAP",
save_html_path="../Figures/Fig3A.html",
save_svg_path="../Figures/Fig3A.svg",
)
Number of Hybrids: 1082 1082
#SUP FIG 7
plot_heights(temp_ht, flower_DAP=flower_GDD, x_col="Cum_GDD [C]", xaxis_title="Cumulative Growing Degree Days [C]",
save_html_path="../Figures/SupFig7.html",
save_svg_path="../Figures/SupFig7.svg",
)
Number of Hybrids: 1082 1082
#FIGURE 3B
import plotly.figure_factory as ff
#create each individual figures
figs_dict = {}
for trait in man_calc_BLUPS["Trait"].unique():
for_plot = []
group_labels = []
for tester in man_calc_BLUPS["Tester"].unique():
tmp = man_calc_BLUPS[(man_calc_BLUPS["Trait"]==trait) & (man_calc_BLUPS["Tester"]==tester)].copy()
for_plot.append(tmp.Data.tolist())
group_labels.append(tmp["Tester"].unique()[0])
fig = ff.create_distplot(for_plot, group_labels, show_hist=False, show_rug=False)
fig["data"][0]['line']['dash'] = "dash"
fig["data"][1]['line']['dash'] = "dot"
fig["data"][2]['line']['dash'] = "dashdot"
fig.update_layout(title=tmp["Trait"].unique()[0])
figs_dict[trait] = fig
#group figures
fig = make_subplots(rows=3, cols=3, subplot_titles=list(figs_dict.keys()))
row_col = [1,1]
for key in figs_dict.keys():
#print(row_col)
#fix legend issues
if row_col != [1,1]:
for x in range(0,len(figs_dict[key].data)):
figs_dict[key].data[x].showlegend=False
fig.add_traces(figs_dict[key].data, rows=[row_col[0]]*3, cols=[row_col[1]]*3)
#fig.add_trace(figs_dict[key].data[0], row=[row_col[0]], col=[row_col[1]])
if row_col[1] < 3:
row_col[1] = row_col[1]+1
else:
row_col[0] = row_col[0]+1
row_col[1] = 1
fig.data[0].legendgrouptitle = dict(text="Tester")
fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))
width = 500
height = 550
fig.update_layout(autosize=False, width=width,height=height)
fig.update_layout(legend=dict(yanchor="top",
y=1.1,
xanchor="left",
x=0,
#entrywidthmode="fraction",
#entrywidth=1,
orientation="h",
#tracegroupgap=0,
#itemwidth=40,
#font_size=5,
))
fig.write_html("../Figures/Fig3B.html")
fig.write_image("../Figures/Fig3B.svg")#, scale=scale)
fig.show()
#FIGURE 3C
#fix ordering for figure
man_VCs["Trait_order"] = man_VCs["Trait"]
man_VCs["Trait_order"] = man_VCs["Trait_order"].replace({
'Asymptote':1,
'Growth rate':2,
'Inflection point':3,
'YLD':4,
'DTA':5,
'DTS':6,
'ASI':7,
'PHT':8,
'EHT':9,
'ASI':10,
'Temporal plant height':11})
man_VCs["VC_order"] = man_VCs["Variance Components"]
man_VCs["VC_order"] = man_VCs["VC_order"].replace({'Hybrid':1,
'Row':5,
'Range':4,
'Rep':6,
'Residual':7,
'Hybrid:Flight':3,
'Flight':2})
man_VCs = man_VCs.sort_values(["VC_order", "Trait_order"], ascending=[False,True])
#setup metrics df
#CV_R2_Rep = man_VCs[["Repetability","Rsquared","CV"]].copy()
#CV_R2_Rep.index = man_VCs["Trait"]
#CV_R2_Rep.drop_duplicates(inplace=True)
#CV_R2_Rep = CV_R2_Rep.stack().reset_index().rename(columns={"level_1":"Metric",0:"Value"})
#CV_R2_Rep["Value"] = CV_R2_Rep["Value"]*100
CV_R2_Rep = man_VCs[["Trait","Repetability","Rsquared","CV"]].copy()
CV_R2_Rep.drop_duplicates(inplace=True)
#create figure
fig1 = px.bar(man_VCs, x="Trait", y="Explaned Percent Variation (%)",
color="Variance Components", #pattern_shape="Variance Components",
)
#fig2 = px.scatter(CV_R2_Rep, x="Trait", y="Value", symbol="Metric")
fig = make_subplots(specs=[[{"secondary_y": True}]])
cv = go.Scatter(x=CV_R2_Rep.Trait, y=CV_R2_Rep.CV, name="CV", mode='markers',
marker=dict(size=12, color = 'white', symbol='square',
line=dict(width=1, color='black')
),
legendgroup="Metrics",
legendgrouptitle_text="Metrics",
)
rep = go.Scatter(x=CV_R2_Rep.Trait, y=CV_R2_Rep.Repetability, name="Repetability", mode='markers',
marker=dict(size=12, color = 'white', symbol='triangle-down',
line=dict(width=1, color='black')
),
legendgroup="Metrics",
)
r2 = go.Scatter(x=CV_R2_Rep.Trait, y=CV_R2_Rep.Rsquared, name="Rsquared", mode='markers',
marker=dict(size=12, color = 'white', symbol='triangle-up',
line=dict(width=1, color='black')
),
legendgroup="Metrics",
)
fig.add_traces([cv, rep, r2], secondary_ys=[True,True,True])
fig.add_traces(fig1.data)
fig.update_layout(barmode='stack')
#seperate legends into two groups
for trace_num in range(0, len(fig.data)):
if fig.data[trace_num].name in man_VCs["Variance Components"].unique():
fig.data[trace_num].legendgroup = "VC"
fig.data[trace_num].legendgrouptitle = dict(text="Variance Components")
#print(fig.data[trace_num].legendgroup)
#print("\t",fig.data[trace_num].name)
#print("\t", fig.data[trace_num].legendgrouptitle)
#fig.update_layout(legend_traceorder="reversed")
fig.update_yaxes(title_text="Explaned Percent Variation (%)", secondary_y=False)
fig.update_yaxes(title_text="Repeatability, Rsquard, and CV", secondary_y=True)
fig.update_layout(margin=dict(l=0, r=1, t=30, b=1))
fig.update_xaxes(tickangle=15)
fig.update_layout(autosize=False, width=800, height=400)
fig.write_html("../Figures/Fig3C.html")
fig.write_image("../Figures/Fig3C.svg")#, scale=scale)
fig.show()